Local Aggregation (LA) [1]} builds on Non-parametric Instance Discrimination [2]} and uses a robust clustering objective (uses a closest neighbors set generated using multiple runs of k-means) similar to consensus clustering to move statistically similar points closer in the representation space and dissimilar points farther away. They demonstrate that the learnt representations are better by evaluating using the linear evaluation protocol on ImageNet.
